Optimal Assignment of Augmented Reality Tasks for Edge-Based Variable Infrastructures

In the last few years, the number of devices connected to the Internet has increased considerably; so has the data interchanged between these devices and the Cloud, as well as energy consumption and the risk of network congestion. The problem can be alleviated by reducing communication between Internet-of-Things devices and the Cloud. Recent paradigms, such as Edge Computing and Fog Computing, propose to move data processing tasks from the Cloud to nearby devices to where data is produced or consumed. One of the main challenges of these paradigms is to cope with the heterogeneity of the infrastructures where tasks can be offloaded. This paper presents a solution for the optimal allocation of computational tasks to edge devices, with the aim of minimizing the energy consumption of the overall application. The heterogeneity is represented and managed by using Feature Models, widely employed in Software Product Lines. Given the application and infrastructure configurations, our Optimal Tasks Assignment Framework generates the optimal task allocation and resources assignment. The resultant deployment represents the most energy efficient configuration at load-time, without compromising the user experience. The scalability and energy saving of the approach are evaluated in the domain of augmented reality applicationsHADAS TIN2015-64841-R (co-funded by FEDER funds), TASOVA MCIU-AEI TIN2017-90644-REDT, MEDEA RTI2018-099213-B-I00 (co-funded by FEDER funds) LEIA UMA18-FEDERJA-157 (co-funded by FEDER funds) Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Optimal Assignment of Augmented Reality Tasks for Edge-Based Variable Infrastructures

In the last few years, the number of devices connected to the Internet has increased considerably; so has the data interchanged between these devices and the Cloud, as well as energy consumption and the risk of network congestion. The problem can be alleviated by reducing communication between Internet-of-Things devices and the Cloud. Recent paradigms, such as Edge Computing and Fog Computing, propose to move data processing tasks from the Cloud to nearby devices to where data is produced or consumed. One of the main challenges of these paradigms is to cope with the heterogeneity of the infrastructures where tasks can be offloaded. This paper presents a solution for the optimal allocation of computational tasks to edge devices, with the aim of minimizing the energy consumption of the overall application. The heterogeneity is represented and managed by using Feature Models, widely employed in Software Product Lines. Given the application and infrastructure configurations, our Optimal Tasks Assignment Framework generates the optimal task allocation and resources assignment. The resultant deployment represents the most energy efficient configuration at load-time, without compromising the user experience. The scalability and energy saving of the approach are evaluated in the domain of augmented reality applications